ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
Leveraging digital media data for pharmacovigilance.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Research on daily medication and drug efficacy evaluation for chronic diseases based on natural language processing (NLP).Frontiers in public health · 2026Article
- The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review.JMIR public health and surveillance · 2024Article
- Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance.Biomolecules · 2023Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of novel drugs in response to changing clinical requirements is a complex and costly method with uncertain outcomes. Postmarket pharmacovigilance is essential as drugs often have under-reported side effects. This study intends to use the power of digital media to discover the under-reported side effects of marketed drugs. We have collected tweets for 11 different Drugs (Alprazolam, Adderall, Fluoxetine, Venlafaxine, Adalimumab, Lamotrigine, Quetiapine, Trazodone, Paroxetine, Metronidazole and Miconazole). We have compiled a vast adverse drug reactions (ADRs) lexicon that is used to filter health related data. We constructed machine learning models for automatically annotating the huge amount of publicly available Twitter data. Our results show that on average 43 known ADRs are shared between Twitter and FAERS datasets. Moreover, we were able to recover on average 7 known side effects from Twitter data that are not reported on FAERS. Our results on Twitter dataset show a high concordance with FAERS, Medeffect and Drugs.com. Moreover, we manually validated some of the under-reported side effect predicted by our model using literature search. Common known and under-reported side effects can be found at https://github.com/cbrl-nuces/Leveraging-digital-media-data-for-pharmacovigilance.
Indexed as
Identifiers
33936417PMC8075481What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.